What does \(k\) mean or do in the equation: \(ks \ln\left(1+\frac{\beta I}{k}\right)\)?
What does an interaction between dose and strain mean in a logistic regression? How does this change if infection is likely or unlikely?
What does the shape parameter, \(\alpha\) do in a Gamma distribution?
This (un)certainty is reflected in prior distributions
What priors say about the distribution of (unobserved) data can be hard to grok
\(\therefore\) use prior predictive simulation
What priors say about the distribution of (unobserved) data can be hard to grok
\(\therefore\) use prior predictive simulation
This (un)certainty is reflected in the posterior distributions
To the extent parameters are meaningful/interpretable on their own (e.g., a slope) you can construct CIs and HDPIs, and other features of the marginal posterior distributions
What they say about expectations on the response scale can be hard to grok
\(\therefore\) plot posterior inference (e.g., against data)
What they say about expectations on the responses scale can be hard to grok
\(\therefore\) plot posterior inference (e.g., against data)
Involves parameter uncertainty + sampling noise.
What they say about the distribution of (future) data can be hard to grok
\(\therefore\) use posterior predictive simulation
What they say about the distribution of (future) data can be hard to grok
\(\therefore\) use posterior predictive simulation